Search

An increasing number of cultural heritage institutions publish data online. Ontologies can be used to structure published data, thereby increasing interoperability. To achieve widespread adoption of ontologies, institutions such as libraries, archives and museums have to be able to assess whether an ontology can adequately capture information about their artifacts. We identify six requirements that should be met by ontologies in the cultural heritage domain, based upon modeling challenges encountered while publishing data of the Rijksmuseum Amsterdam and challenges observed in related work. These challenges regard specialization, object- and event-centric approaches, temporality, representations, views and subject matter. For each challenge, we investigate common modeling approaches, by discussing two models regularly used in the museum sector: the CIDOC Conceptual Reference Model and the Europeana Data Model. The outlined approaches and requirements provide insights for data modeling practices reaching beyond the cultural heritage sector.

An increasing number of cultural heritage institutions publish data online. Ontologies can be used to structure published data, thereby increasing interoperability. To achieve widespread adoption of ontologies, institutions such as libraries, archives and museums have to be able to assess whether an ontology can adequately capture information about their artifacts. We identify six requirements that should be met by ontologies in the cultural heritage domain, based upon modeling challenges encountered while publishing data of the Rijksmuseum Amsterdam and challenges observed in related work. These challenges regard specialization, object- and event-centric approaches, temporality, representations, views and subject matter. For each challenge, we investigate common modeling approaches, by discussing two models regularly used in the museum sector: the CIDOC Conceptual Reference Model and the Europeana Data Model. The outlined approaches and requirements provide insights for data modeling practices reaching beyond the cultural heritage sector.

This paper describes BiographyNet, a digital humanities project (2012-2016) that brings together researchers from history, computational linguistics and computer science. The project uses data from the Biography Portal of the Netherlands (BPN), which contains approximately 125,000 biographies from a variety of Dutch biographical dictionaries from the eighteenth century until now, describing around 76,000 individuals. BiographyNet's aim is to strengthen the value of the portal and comparable biographical datasets for historical research, by improving the search options and the presentation of its outcome, with a historically justified NLP pipeline that works through a user evaluated demonstrator. The project's main target group are professional historians. The project therefore worked with two key concepts: "provenance" -understood as a term allowing for both historical source criticism and for references to data-management and programming interventions in digitized sources; and "perspective" interpreted as inherent uncertainty concerning the interpretation of historical results., Comment: 35 pages, 5 figures, \'A. Z. Bern\'ad, C. Gruber, M. Kaiser (editors). Europa baut auf Biographien: Aspekte, Bausteine, Normen und Standards f\"ur eine europ\"aische Biographik (2017)

This paper describes BiographyNet, a digital humanities project (2012-2016) that brings together researchers from history, computational linguistics and computer science. The project uses data from the Biography Portal of the Netherlands (BPN), which contains approximately 125,000 biographies from a variety of Dutch biographical dictionaries from the eighteenth century until now, describing around 76,000 individuals. BiographyNet's aim is to strengthen the value of the portal and comparable biographical datasets for historical research, by improving the search options and the presentation of its outcome, with a historically justified NLP pipeline that works through a user evaluated demonstrator. The project's main target group are professional historians. The project therefore worked with two key concepts: "provenance" -understood as a term allowing for both historical source criticism and for references to data-management and programming interventions in digitized sources; and "perspective" interpreted as inherent uncertainty concerning the interpretation of historical results.

This paper describes BiographyNet, a digital humanities project (2012-2016) that brings together researchers from history, computational linguistics and computer science. The project uses data from the Biography Portal of the Netherlands (BPN), which contains approximately 125,000 biographies from a variety of Dutch biographical dictionaries from the eighteenth century until now, describing around 76,000 individuals. BiographyNet's aim is to strengthen the value of the portal and comparable biographical datasets for historical research, by improving the search options and the presentation of its outcome, with a historically justified NLP pipeline that works through a user evaluated demonstrator. The project's main target group are professional historians. The project therefore worked with two key concepts: "provenance" -understood as a term allowing for both historical source criticism and for references to data-management and programming interventions in digitized sources; and "perspective" interpreted as inherent uncertainty concerning the interpretation of historical results.

This paper describes BiographyNet, a digital humanities project (2012-2016) that brings together researchers from history, computational linguistics and computer science. The project uses data from the Biography Portal of the Netherlands (BPN), which contains approximately 125,000 biographies from a variety of Dutch biographical dictionaries from the eighteenth century until now, describing around 76,000 individuals. BiographyNet's aim is to strengthen the value of the portal and comparable biographical datasets for historical research, by improving the search options and the presentation of its outcome, with a historically justified NLP pipeline that works through a user evaluated demonstrator. The project's main target group are professional historians. The project therefore worked with two key concepts: "provenance" -understood as a term allowing for both historical source criticism and for references to data-management and programming interventions in digitized sources; and "perspective" interpreted as inherent uncertainty concerning the interpretation of historical results.

In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvia's curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision.

It is possible to automatically annotate a natural science spreadsheet using lexical matching, given that the tables in these spreadsheets meet a number of requirements regarding the content. Results of a survey show that most of the existing natural science spreadsheets deviate from the ideal situation. We propose to complement lexical matching with both heuristics and knowledge from external vocabularies to overcome these deviations.

With more and more cultural heritage data being published online, their usefulness in this open context depends on the quality and diversity of descriptive metadata for collection objects. In many cases, existing metadata is not adequate for a variety of retrieval and research tasks and more specific annotations are necessary. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used for eliciting simple annotations, identifying people with the required expertise might prove troublesome for tasks requiring more complex or domain-specific knowledge. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation tool for experts and 3) validation of the methodology and tool in three case studies. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high quality annotations in a variety of domains and annotation tasks. A user evaluation indicates the tool is suited and usable for domain specific annotation tasks.

Simple Knowledge Organization System (SKOS) provides a data model and vocabulary for expressing Knowledge Organization Systems (KOSs) such as thesauri and classification schemes in Semantic Web applications. This paper presents the main components of SKOS and their formal expression in Web Ontology Language (OWL), providing an extensive account of the design decisions taken by the Semantic Web Deployment (SWD) Working Group of the World Wide Web Consortium (W3C), which between 2006 and 2009 brought SKOS to the status of W3C Recommendation. The paper explains key design principles such as "minimal ontological commitment" and systematically cites the requirements and issues that influenced the design of SKOS components. By reconstructing the discussion around alternative features and design options and presenting the rationale for design decisions, the paper aims at providing insight into how SKOS turned out as it did, and why. Assuming that SKOS, like any other successful technology, may eventually be subject to revision and improvement, the critical account offered here may help future editors approach such a task with deeper understanding., Comment: Submitted to Journal of Web Semantics, 2012-02-05

Problem-solving methods provide reusable architectures and components for implementing the reasoning part of knowledge-based systems. The UNIFIED PROBLEM-SOLVING METHOD DESCRIPTION LANGUAGE (UPML) has been developed to describe and implement such architectures and components to facilitate their semi-automatic reuse and adaptation. In a nutshell, UPML is a framework for developing knowledge-intensive reasoning systems based on libraries ofg eneric problem-solving components. The paper describes the components and adapters, architectural constraints, development guidelines, and tools provided by UPML. UPML is developed as part of the IBROW project, which provides an Internet-based brokering service for reusing problem-solving methods.

Problem-solving methods provide reusable architectures and components for implementing the reasoning part of knowledge-based systems. The UNIFIED PROBLEM-SOLVING METHOD DESCRIPTION LANGUAGE (UPML) has been developed to describe and implement such architectures and components to facilitate their semi-automatic reuse and adaptation. In a nutshell, UPML is a framework for developing knowledge-intensive reasoning systems based on libraries ofg eneric problem-solving components. The paper describes the components and adapters, architectural constraints, development guidelines, and tools provided by UPML. UPML is developed as part of the IBROW project, which provides an Internet-based brokering service for reusing problem-solving methods.